Missing Data Strategy Comparator
Overview
The Missing Data Strategy Comparator helps you understand the impact of different missing data handling methods on your analysis. Generate datasets with various missingness patterns (MCAR, MAR, MNAR), apply different imputation strategies, and visualize how each method affects the data distribution, mean, variance, and correlations. This tool is essential for making informed decisions about handling missing data in your statistical analyses.
Tips
- MCAR (Missing Completely At Random) is the least problematic - simple deletion methods work fine with minimal bias
- MAR (Missing At Random) requires more sophisticated methods like regression imputation to avoid bias in estimates
- MNAR (Missing Not At Random) is most challenging - bias is unavoidable regardless of method; consider sensitivity analyses
- Mean imputation always reduces variance and can artificially strengthen correlations - use with extreme caution
- Listwise deletion reduces sample size dramatically when missingness is spread across variables, reducing statistical power
- Multiple imputation (not shown here) is generally preferred over single imputation methods in real analyses
- The “best” method depends on your missingness mechanism, analysis goals, and acceptable bias-variance tradeoff